Complete Machine Learning Course in English > Introduction

Environment Setup Part 2

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Overview

Hello,

I am Kushal from LearnVern.( 6 seconds pause ; music )

You are welcome to the course of Machine learning.

This tutorial is a continuation of the previous session.

So, let us see ahead.

Now, here we can see that the installation process of anaconda is completed.

Now, here you have to just click on next.

Then after that, again you will click on next.

Here appears, "anaconda individual edition tutorial" and "getting started with anaconda" which is selected by default.

So, we will keep it untouched.

And then here we will click on finish.

With this anaconda installation is completed.

Now, it's taking us to its website.

Where, it will tell us about tutorials and anaconda clouds.

So, we will learn about this sometime later.

Now, we will check and test whether the installation of anaconda is completed or not.

I will click on windows, and here I will type anaconda.

Here, you can see "anaconda prompt"

And "anaconda navigator"

Now, "anaconda prompt" will appear as a CMD command prompt in black screen, where you can write your command, and if you have to do any installation or setup by using pip and conda, which are utilities, that we will understand later, that you can do here.

Okay , guys !

Now, I will open "anaconda Navigator".

So let me open the anaconda navigator.

Now, this is a Utility which helps us with so many tools because as I had explained to it, it is a data science tool kit.

So, a toolkit means it is going to give us many softwares.

Along with python, vs code, r programming it also gives us r studio.

So let's wait so that it opens.

You will see black screens which will keep appearing and this is a normal process.

Now, you will see a green icon of anaconda after this anaconda window will open.

So, some processes are still undergoing and now the wait is almost over and we will have the window soon.

Now , you can see the wait is over and the window is in front of us.

I will minimise this prompt.

And, here you can see we have anaconda prompt here, we have datalore, IBM watson studio cloud, jupyterlab, jupyter notebook, powershell prompt.

We have so many tools that will help in creating so many applications and programmes for data science and machine learning.

We are going to use one of these tools for learning.

We will use a jupyter notebook and here I will click on the launch item after which the jupyter notebook will be launched.

If you wish to open a jupyter notebook without opening an anaconda then you can directly do so by clicking on windows and by typing jupyter notebook and from here also u can open it directly.

Understood guys! Great !

So these are the two methods that is,

First you can open an anaconda and use any tool, otherwise you can directly go to windows and open a jupyter notebook.

So, I will give a little bit of details about the jupyter notebook.

This is its home screen, here is its quit or log out, when you are done with your work you can quit or log out.

Otherwise these are folders and files for working directories and, here you have to go on new and click on python 3, to open a new notebook.

We will learn about it in detail in the next sessions.

For now, we will write a test code over here i.e print and in brackets hello world, H E L L O W O R L D and execute this.

You can see it's working perfectly.

So my jupyter notebook is also installed over here.

So, friends let's conclude here for today,

Today's session will end here.

And it's another part we will see in the next session.

So keep learning and remain motivated

Thank you.

If you have any queries or comments, click the discussion button below the video and post there. This way, you will be able to connect to fellow learners and discuss the course. Also, Our Team will try to solve your query.

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Course Content

  1. Getting Started with Machine Learning
    4m 4s
  2. How to use LearnVern
    3m 8s

  1. Introduction to Machine Learning
    16m 19s
  2. Environment Setup Part 1
    3m 21s
  3. Environment Setup Part 2
    4m 52s
  4. Environment Setup Part 3
    2m 35s

  1. Data Wrangling
    12m 36s
  2. Importing Libraries and Dataset
    11m 35s
  3. Handling Missing Data
    12m 13s
  4. Handling Missing Data - Practical
    19m 46s
  5. Encoding Categorical Data
    7m 3s
  6. Encoding Catergorical Data - Practical
    8m 6s
  7. Splitting Dataset
    3m 21s
  8. Splitting Dataset - Practical
    9m 43s
  9. Normalizing the Data - Part 1
    7m 29s
  10. Normalizing the Data - Part 2
    7m 30s
  11. Finding Machine Learning Datasets
    8m 56s
  12. Exploratory Data Analysis
    9m 49s
  13. Plotting Graphs - Part 1
    17m 51s
  14. Plotting Graphs - Part 2
    21m 44s
  15. Distribution Models - Part 1
    29m 48s
  16. Distribution Models - Part 2
    10m 23s
  17. Assignment : Data Preprocessing for Machine Learning
    1m 54s

  1. Machine Learning Paradigms
    10m 37s
  2. Assignment : Machine Learning Paradigms
    3m 22s

  1. Decision Tree Iterative Dichotomiser 3
    20m 2s
  2. Random Forest
    5m 25s
  3. Support Vector Machine Classifier
    6m 22s
  4. Support Vector Machine Classifier - Practical 1
    16m 16s
  5. Support Vector Machine Classifier - Practical 2
    10m 25s
  6. Naive Bayes Classifier
    6m 36s
  7. Naive Bayes Classifier - Practical 1
    14m 34s
  8. Naive Bayes Classifier - Practical 2
    9m 42s
  9. Evaluating Classification Models Performance
    9m 8s
  10. Evaluating Classification Models Performance - Practical
    29m 33s
  11. Overview of Classification
    6m 16s
  12. Logistic Regression
    5m 35s
  13. Logistic Regression - Practical - 1
    30m 22s
  14. Logistic Regression - Practical - 2
    13m 28s
  15. KNN
    7m 34s
  16. KNN Practical - 1
    29m 39s
  17. KNN - Practical 2
    9m 53s
  18. Decision Trees for Classification
    6m 1s
  19. Decision Trees for Classification - Practical 1
    23m 27s
  20. Decision Trees for Classification - Practical 2
    18m 39s
  21. Assignment : Supervised Learning Algorithms
    1m 34s

  1. Simple Linear Regression
    10m 4s
  2. Simple Linear Regression - Practical
    11m 46s
  3. Salary Prediction using Linear Regression
    19m 25s
  4. Multi-Linear Regression
    2m 56s
  5. Startup Prediction using Multiple Regression
    20m 34s
  6. Support Vector Regressor
    11m 15s
  7. Support Vector Regressor - Practical 1
    7m 28s
  8. Support Vector Regressor - Practical 2
    14m 44s
  9. Decision Tree Regressor
    5m 31s
  10. Decision Tree Regressor - Practical 1
    5m 33s
  11. Decision Tree Regressor - Practical 2
    16m 31s
  12. Regressor Model Selection
    11m 8s
  13. Evaluating Regression Model Performance
    10m 45s
  14. Evaluating Regression Model Performance - Practical
    12m 11s
  15. Assignment : Regression Algorithms
    1m 5s

  1. Distance Metrics
    6m 1s
  2. K-Means Clustering
    11m 40s
  3. K-Means Clustering - Practical
    9m 32s
  4. Mall Customers Prediction using K Means Clustering
    16m 42s
  5. Hierarchical Clustering - Agglomerative , Divisive
    5m 25s
  6. Agglomerative Clustering - Practical
    4m 37s
  7. Divisive Clustering - Practical
    13m 43s
  8. DBscan Spatial Clustering
    10m 53s
  9. Mall Customers Prediction using Hierarchical Clustering
    17m 48s
  10. Assignment : Unsupervised Learning Algorithms
    3m 11s

  1. Association Rule Learning - Apriori, FP Growth
    8m 17s
  2. Association Rule Learning - Apriori Practical
    7m 41s
  3. Market Basket Analysis using Apriori
    19m 30s
  4. FP Growth
    8m
  5. Market Basket Analysis using FP Growth
    12m 20s
  6. Assignment : Association Rule Mining
    56s

  1. Reinforcement Learning Theory - Multi Armed Bandits
    27m 30s
  2. Upper Confidence Bound - Practical
    10m 53s
  3. Thompson Sampling - Practical
    5m 34s
  4. Q Learning
    32m 24s
  5. Assignment : Reinforcement Learning
    1m 43s

  1. Overview of Dimensoionality Reduction
    4m 40s
  2. Princinpal Component Analysis
    39m 4s
  3. Principal Component Analysis - Practical
    6m 37s
  4. Linear Discriminant Analysis
    19m 9s
  5. Linear Discriminant Analysis - Practical
    6m 29s
  6. Assignment : Dimensionality Reduction
    52s

  1. Basics of Regularization and Optimization
    7m 58s
  2. Cross Validation
    20m 3s
  3. Hyperparameter Tuning
    20m 7s
  4. Sampling Methods
    19m 8s
  5. Underfitting and Overfitting in Models
    5m 33s
  6. Variance and Bias
    9m 33s
  7. Assignment : Regularization and Optimization
    1m 1s

  1. Advance Trends in Machine Learning
    12m 48s

  1. Introduction to Keras and Deep Learning
    9m 2s
  2. Practical Demonstration -Keras
    18m 35s

  1. Reinforcement Learning Project - Teach a Taxi Part 1
    18m 10s
  2. Reinforcement Learning Project - Teach a Taxi Part 2
    15m 11s
  3. Reinforcement Learning Project - Teach a Taxi Part 3
    14m 35s
  4. Reinforcement Learning Project - Teach a Taxi Part 4
    17m 23s
  5. Loan Prediction Project Part 1
    32m 16s
  6. Loan Prediction Project Part 2
    28m 42s

  1. Course Summary
    3m 53s

  1. Interview Questions Part 1
    18m 22s
  2. Interview Questions Part 2
    13m 23s
  3. Interview Questions Part 3
    16m 2s

  1. Career Guidelines
    6m 14s
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